OpenAI-compatible Model APIs
Use a familiar API surface for curated frontier open-weight models. The documented setup is to create an API key and send inference requests, with no inference infrastructure to manage.
FriendliAI
Freemium
FriendliAI is an inference cloud for deploying frontier open-weight and custom AI models in production. It offers serverless Model APIs, dedicated GPU endpoints, and BYOG options for agent, multimodal, and other high-throughput workloads.
What is FriendliAI?
FriendliAI is an AI inference cloud for serving frontier open-weight and custom models in production. It provides three main deployment paths: serverless Model APIs, Dedicated Endpoints on dedicated GPUs, and Bring Your Own GPU (BYOG). The platform is intended for teams building agent, multimodal, and other inference workloads that require scalable throughput, low-latency responses, and managed operations.
Model APIs offer ready-to-use, OpenAI-compatible access to a curated model catalog, while Dedicated Endpoints provide dedicated capacity for predictable performance, custom models, and deployment control. FriendliAI also supports structured generation features such as JSON mode, function and tool calling, and schema-guided outputs. Its documented infrastructure includes multi-cloud and multi-region redundancy, automated failover, and monitoring.
What can FriendliAI do?
Dedicated GPU inference
Deploy models on dedicated GPU instances when a workload needs predictable throughput, isolation, or support for custom model serving. Available deployment modes include on-demand and enterprise reserved capacity.
Agent and structured-generation support
Model APIs support JSON mode, function and tool calling, and schema-guided outputs. The documentation also describes broad model support, strict schema enforcement, and parallel tool calls.
Multimodal model access
The platform presents a single API surface for text, vision, and other supported modalities, allowing teams to build applications that use more than text generation.
Inference performance stack
FriendliAI identifies custom GPU kernels, smart caching, continuous batching, speculative decoding, parallel inference, and infrastructure-level caching as components of its inference optimization approach.
Multi-region reliability and scaling
The service describes multi-cloud, multi-region architecture with active redundancy, automated failover, fast recovery, monitoring, and autoscaling for changing traffic levels.
Use Cases
“Production AI agents”
Build agents that need streaming responses, long-context inference, function or tool calling, and structured outputs. Model APIs provide a starting point without requiring the team to operate GPU infrastructure.
“Coding agents”
Connect coding-agent workflows to open-weight models through FriendliAI’s documented FriendliLink setup and agent examples, then use the API for model-driven coding tasks.
“Custom model deployment”
Serve a fine-tuned or proprietary model through Dedicated Endpoints when the application requires dedicated GPU capacity and more deployment control than a shared serverless API.
“High-volume or variable traffic”
Use Model APIs to begin serverless and scale with demand, or move to Dedicated Endpoints when the application needs more predictable throughput for sustained inference workloads.
“Multimodal applications”
Use the platform’s text and vision model access for applications that combine language generation with visual inputs, subject to the capabilities of the selected model.
Frequently Asked Questions
How do I get started with FriendliAI?
Sign up for FriendliAI, create an API key in the API Keys settings page, and send an initial inference request. The documentation provides guides, examples, and API references for building with Model APIs and Dedicated Endpoints.
Which models are available?
FriendliAI lists frontier open-weight models from families including GLM, MiniMax, Kimi, DeepSeek, Qwen, and Gemma. Its site also says Dedicated Endpoints support custom models and more than 610,000 open-source models; availability and pricing depend on the selected serving option.
Can existing OpenAI-compatible applications use FriendliAI?
Yes. Model APIs are described as OpenAI-compatible. The product page says teams can swap the base URL so compatible code can send requests through FriendliAI.
When should I use a Dedicated Endpoint instead of a Model API?
Model APIs are intended for quick, serverless access to curated models without infrastructure management. Dedicated Endpoints are the better fit when you need dedicated GPU capacity, predictable throughput, custom model serving, or deployment options such as on-demand and reserved instances.
How is Dedicated Endpoint usage billed?
Dedicated Endpoints use on-demand GPU billing metered by the second, and the pricing page states that customers pay only while the GPU is active with no extra startup-time charge. Listed GPU hourly rates vary by GPU type, while enterprise reserved and BYOG arrangements use separate commercial terms.
Quick Facts
- Product type
- Managed AI inference cloud
- Deployment options
- Model APIs, Dedicated Endpoints, and BYOG
- API compatibility
- OpenAI-compatible Model APIs
- Supported model scope
- Frontier open-weight models, custom models, and a catalog of 610,000+ open-source models
- Documented output capabilities
- Text and vision inference, JSON mode, function/tool calling, and schema-guided outputs
- Source domain
- friendli.ai
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